External Sorting

1. External Sorting R & G Chapter 11 One of the advantages of being disorderly is that one is constantly making exciting discoveries. A. A. Milne

2. Why Sort? • A classic problem in computer science! • Data requested in sorted order • e.g., find students in increasing gpa order • Sorting is first step in bulk loading B+ tree index. • Sorting useful for eliminating duplicate copies in a collection of records (Why?) • Sorting is useful for summarizing related groups of tuples • Sort-merge join algorithm involves sorting. • Problem: sort 1Gb of data with 1Mb of RAM. • why not virtual memory?

3. Streaming Data Through RAM • An important detail for sorting & other DB operations • Simple case: • Compute f(x) for each record, write out the result • Read a page from INPUT to Input Buffer • Write f(x) for each item to Output Buffer • When Input Buffer is consumed, read another page • When Output Buffer fills, write it to OUTPUT • Reads and Writes are not coordinated • E.g., if f() is Compress(), you read many pages per write. • E.g., if f() is DeCompress(), you write many pages per read. Input Buffer Output Buffer f(x) INPUT OUTPUT RAM

4. 2-Way Sort • Pass 0: Read a page, sort it, write it. • only one buffer page is used (as in previous slide) • Pass 1, 2, …, etc.: • requires 3 buffer pages • merge pairs of runs into runs twice as long • three buffer pages used. INPUT 1 OUTPUT INPUT 2 Main memory buffers Disk Disk

5. Two-Way External Merge Sort 6,2 2 Input file 9,4 8,7 5,6 3,1 3,4 PASS 0 1,3 2 1-page runs 2,6 4,9 7,8 5,6 3,4 • Each pass we read + write each page in file. • N pages in the file => the number of passes • So total cost is: • Idea:Divide and conquer: sort subfiles and merge PASS 1 4,7 1,3 2,3 2-page runs 8,9 5,6 2 4,6 PASS 2 2,3 4,4 1,2 4-page runs 6,7 3,5 6 8,9 PASS 3 1,2 2,3 3,4 8-page runs 4,5 6,6 7,8 9

6. General External Merge Sort • More than 3 buffer pages. How can we utilize them? • To sort a file with N pages using B buffer pages: • Pass 0: use B buffer pages. Produce sorted runs of B pages each. • Pass 1, 2, …, etc.: merge B-1 runs. INPUT 1 . . . . . . INPUT 2 . . . OUTPUT INPUT B-1 Disk Disk B Main memory buffers

7. Cost of External Merge Sort • Number of passes: • Cost = 2N * (# of passes) • E.g., with 5 buffer pages, to sort 108 page file: • Pass 0: = 22 sorted runs of 5 pages each (last run is only 3 pages) • Now, do four-way (B-1) merges • Pass 1: = 6 sorted runs of 20 pages each (last run is only 8 pages) • Pass 2: 2 sorted runs, 80 pages and 28 pages • Pass 3: Sorted file of 108 pages

8. Number of Passes of External Sort ( I/O cost is 2N times number of passes)

9. Internal Sort Algorithm • Quicksort is a fast way to sort in memory. • Alternative: “tournament sort” (a.k.a. “heapsort”, “replacement selection”) • Keep two heaps in memory, H1 and H2 read B-2 pages of records, inserting into H1; while (records left) { m = H1.removemin(); put m in output buffer; if (H1 is empty) H1 = H2; H2.reset(); start new output run; else read in a new record r (use 1 buffer for input pages); if (r < m) H2.insert(r); else H1.insert(r); } H1.output(); start new run; H2.output();

10. B More on Heapsort • Fact: average length of a run is 2(B-2) • The “snowplow” analogy • Worst-Case: • What is min length of a run? • How does this arise? • Best-Case: • What is max length of a run? • How does this arise? • Quicksort is faster, but … longer runs often means fewer passes!

11. I/O for External Merge Sort • Actually, doing I/O a page at a time • Not an I/O per record • In fact, read a block (chunk) of pages sequentially! • Suggests we should make each buffer (input/output) be a chunk of pages. • But this will reduce fan-out during merge passes! • In practice, most files still sorted in 2-3 passes.

12. Number of Passes of Optimized Sort • Block size = 32, initial pass produces runs of size 2B.

13. Sorting Records! • Sorting has become a blood sport! • Parallel sorting is the name of the game ... • Minute Sort: how many 100-byte records can you sort in a minute? • Typical DBMS: 10MB (~100,000 records) • Current World record: 21.8 GB • 64 dual-processor Pentium-III PCs (1999) • Penny Sort: how many can you sort for a penny? • Current world record: 12GB • 1380 seconds on a $672 Linux/Intel system (2001) • $672 spread over 3 years = 1404 seconds/penny • See http://research.microsoft.com/barc/SortBenchmark/

14. Using B+ Trees for Sorting • Scenario: Table to be sorted has B+ tree index on sorting column(s). • Idea: Can retrieve records in order by traversing leaf pages. • Is this a good idea? • Cases to consider: • B+ tree is clusteredGood idea! • B+ tree is not clusteredCould be a very bad idea!

15. Clustered B+ Tree Used for Sorting • Cost: root to the left-most leaf, then retrieve all leaf pages (Alternative 1) • If Alternative 2 is used? Additional cost of retrieving data records: each page fetched just once. Index (Directs search) Data Entries ("Sequence set") Data Records • Better than external sorting!

16. Unclustered B+ Tree Used for Sorting • Alternative (2) for data entries; each data entry contains rid of a data record. In general, one I/O per data record! Index (Directs search) Data Entries ("Sequence set") Data Records

17. External Sorting vs. Unclustered Index • p: # of records per page • B=1,000 and block size=32 for sorting • p=100 is the more realistic value.

18. Summary • External sorting is important; DBMS may dedicate part of buffer pool for sorting! • External merge sort minimizes disk I/O cost: • Pass 0: Produces sorted runs of size B(# buffer pages). Later passes: merge runs. • # of runs merged at a time depends on B, and block size. • Larger block size means less I/O cost per page. • Larger block size means smaller # runs merged. • In practice, # of passes rarely more than 2 or 3.

19. Summary, cont. • Choice of internal sort algorithm may matter: • Quicksort: Quick! • Heap/tournament sort: slower (2x), longer runs • The best sorts are wildly fast: • Despite 40+ years of research, we’re still improving! • Clustered B+ tree is good for sorting; unclustered tree is usually very bad.